Free Learning Spark: Lightning-Fast Big Data Analysis Ebooks Online

                
              
Free 
Learning Spark: Lightning-Fast Big Data Analysis Ebooks Online
Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shellLeverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlibUse one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and StormLearn how to deploy interactive, batch, and streaming applicationsConnect to data sources including HDFS, Hive, JSON, and S3Master advanced topics like data partitioning and shared variables
Paperback: 276 pages
Publisher: O'Reilly Media; 1 edition (February 27, 2015)
Language: English
ISBN-10: 1449358624
ISBN-13: 978-1449358624
Product Dimensions:  7 x 0.6 x 9.2 inches
Shipping Weight: 1 pounds (View shipping rates and policies)
Average Customer Review:  4.0 out of 5 stars  See all reviews (51 customer reviews)
Best Sellers Rank: #10,304 in Books (See Top 100 in Books)   #5 in Books > Computers & Technology > Programming > Languages & Tools > Java   #8 in Books > Computers & Technology > Business Technology > Software > Enterprise Applications   #8 in Books > Computers & Technology > Databases & Big Data > Data Processing
The textual components are well-written. However, the book tends to gloss over examples, providing the same, or similar, snippets to what's on the web site instead of providing full, working examples. This was a poor choice for a "learning" title. Instead of being able to work through each example, I found myself having to scroll around trying to figure out what was missing from each snippet, and how to put together working code. Also, the book presents Scala, Java, and Python snippets throughout the early chapters, which is very distracting. I found myself having to mentally context-switch between the three languages instead of being able to following one all the way through. Would I buy it again? Maybe. There are just a few Spark books, and they're all pretty meh. You have to learn it somewhere, I guess.
I found this volume to be an excellent reference book for a Spark learner like me. I am a software developer, and several reviews suggested that this volume was too basic. I shouldn't have followed their advice. I bought an "advanced" book, instead, only to find myself left without material to fill in some important gaps. The information that is available on the Internet is great, but this book brings much of it together in one place. If you want to learn to think like a Spark programmer--*not* the same as thinking like a programmer--this is the place to begin.
I feel this is a decent compilation of the resources available over the internet. That way, it reduces the time needed for getting started with Spark. This book is definitely suitable anyone new to Spark and Big Data Processing. But for someone who has already worked with Spark and faced some challenges, this may not be helpful.
There are notions popped out out-of-the-blue throughout the book. One example is "accumulators". It is first seen in Chapter 3, while describing the aggregate function, yet lacking any definition of it. You have to go and check the programming guide on the official web page. The book is not comprehensive, but it is more like a companion.
This seems to be the definitive overview of Spark, and fortunately it's well written. It includes basics like setting up a Spark cluster, and topics like streaming and machine learning. I'm glad I read it.
Good text. I've purchased pretty much all of the other options out there-- while this book is still lacks detail (and borrows much of its content from the Databricks examples & Spark documentation available online) it's a worthwhile investment. The detailed treatment of RDDs, SparkSQL and streaming capability alone is enough to justify the cost.
The only reason for the 4-star rating and not higher is that the book is already a bit outdated (from a Scala perspective). Running newer versions of Spark do not support some of the examples in the book. This does not change or distort the overall big picture of the book, however. Still a very intuitive and straight forward intro to Spark.
Disclaimer: I only write reviews when the item is a 5 star.This book is a very comprehensive introduction to Apache Spark. It covers srapk internals in detail. Armed with this knowledge, one can confidently advance to studying and implementing advanced machine learning application on Spark
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